Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions.To solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization model.Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph.The DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods.Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

Dynamic Regularity and Prognoses of Landslide

Dynamic regularity is discussed tightly combining with method and principle of displacement monitoring for landslide . By the principle of dynamic energy analysis is performed emphatically for the broken - line condi on of sliding surface being always made of multiple combination of unit geostructural planes with different dip angles .Several formulae are derived from given conditions and , presented to describe the dynamic regularity . Based on the regularity an example of huge landslide is cited to calculate water urge height of reservoir . By Poisson cycle principle the latter was made for another large slide . The results showed themselves to have very approached vis-a-vis the actual ones .